[Misc] format and refactor some examples (#16252)

Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>

examples/offline_inference/neuron_int8_quantization.py

@@ -22,31 +22,40 @@ prompts = [
 # Create a sampling params object.
 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
-# Create an LLM.
-llm = LLM(
-    model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
-    max_num_seqs=8,
-    # The max_model_len and block_size arguments are required to be same as
-    # max sequence length when targeting neuron device.
-    # Currently, this is a known limitation in continuous batching support
-    # in transformers-neuronx.
-    # TODO(liangfu): Support paged-attention in transformers-neuronx.
-    max_model_len=2048,
-    block_size=2048,
-    # The device can be automatically detected when AWS Neuron SDK is installed.
-    # The device argument can be either unspecified for automated detection,
-    # or explicitly assigned.
-    device="neuron",
-    quantization="neuron_quant",
-    override_neuron_config={
-        "cast_logits_dtype": "bfloat16",
-    },
-    tensor_parallel_size=2)
-# Generate texts from the prompts. The output is a list of RequestOutput objects
-# that contain the prompt, generated text, and other information.
-outputs = llm.generate(prompts, sampling_params)
-# Print the outputs.
-for output in outputs:
-    prompt = output.prompt
-    generated_text = output.outputs[0].text
-    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+def main():
+    # Create an LLM.
+    llm = LLM(
+        model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+        max_num_seqs=8,
+        # The max_model_len and block_size arguments are required to be same as
+        # max sequence length when targeting neuron device.
+        # Currently, this is a known limitation in continuous batching support
+        # in transformers-neuronx.
+        # TODO(liangfu): Support paged-attention in transformers-neuronx.
+        max_model_len=2048,
+        block_size=2048,
+        # ruff: noqa: E501
+        # The device can be automatically detected when AWS Neuron SDK is installed.
+        # The device argument can be either unspecified for automated detection,
+        # or explicitly assigned.
+        device="neuron",
+        quantization="neuron_quant",
+        override_neuron_config={
+            "cast_logits_dtype": "bfloat16",
+        },
+        tensor_parallel_size=2)
+    # Generate texts from the prompts. The output is a list of RequestOutput objects
+    # that contain the prompt, generated text, and other information.
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    print("-" * 50)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 50)
+
+
+if __name__ == "__main__":
+    main()